# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

import paddle

from ppdiffusers import UNet1DModel
from ppdiffusers.utils import floats_tensor, slow

from .test_modeling_common import ModelTesterMixin


class UNet1DModelTests(ModelTesterMixin, unittest.TestCase):
    model_class = UNet1DModel

    @property
    def dummy_input(self):
        batch_size = 4
        num_features = 14
        seq_len = 16
        noise = floats_tensor((batch_size, num_features, seq_len))
        time_step = paddle.to_tensor([10] * batch_size)
        return {"sample": noise, "timestep": time_step}

    @property
    def input_shape(self):
        return 4, 14, 16

    @property
    def output_shape(self):
        return 4, 14, 16

    def test_ema_training(self):
        pass

    def test_training(self):
        pass

    def test_determinism(self):
        super().test_determinism()

    def test_outputs_equivalence(self):
        super().test_outputs_equivalence()

    def test_from_save_pretrained(self):
        super().test_from_save_pretrained()

    def test_model_from_pretrained(self):
        super().test_model_from_pretrained()

    def test_output(self):
        super().test_output()

    def prepare_init_args_and_inputs_for_common(self):
        init_dict = {
            "block_out_channels": (32, 64, 128, 256),
            "in_channels": 14,
            "out_channels": 14,
            "time_embedding_type": "positional",
            "use_timestep_embedding": True,
            "flip_sin_to_cos": False,
            "freq_shift": 1.0,
            "out_block_type": "OutConv1DBlock",
            "mid_block_type": "MidResTemporalBlock1D",
            "down_block_types": (
                "DownResnetBlock1D",
                "DownResnetBlock1D",
                "DownResnetBlock1D",
                "DownResnetBlock1D",
            ),
            "up_block_types": ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D"),
            "act_fn": "mish",
        }
        inputs_dict = self.dummy_input
        return init_dict, inputs_dict

    def test_from_pretrained_hub(self):
        model, loading_info = UNet1DModel.from_pretrained(
            "bglick13/hopper-medium-v2-value-function-hor32",
            output_loading_info=True,
            subfolder="unet",
        )
        self.assertIsNotNone(model)
        self.assertEqual(len(loading_info["missing_keys"]), 0)
        image = model(**self.dummy_input)
        assert image is not None, "Make sure output is not None"

    def test_output_pretrained(self):
        model = UNet1DModel.from_pretrained("bglick13/hopper-medium-v2-value-function-hor32", subfolder="unet")
        paddle.seed(0)
        num_features = model.config.in_channels
        seq_len = 16
        noise = paddle.randn(shape=(1, seq_len, num_features)).transpose(perm=[0, 2, 1])
        time_step = paddle.full(shape=(num_features,), fill_value=0)
        with paddle.no_grad():
            output = model(noise, time_step).sample.permute(0, 2, 1)
        output_slice = output[0, -3:, -3:].flatten()
        expected_output_slice = paddle.to_tensor(
            [
                -0.2857576608657837,
                -0.9908187389373779,
                0.2976357340812683,
                -0.8677187561988831,
                -0.21778395771980286,
                0.08095654845237732,
                -0.5871752500534058,
                0.3299727439880371,
                -0.17421625554561615,
            ]
        )
        self.assertTrue(paddle.allclose(output_slice, expected_output_slice, rtol=0.001))

    def test_forward_with_norm_groups(self):
        pass

    # TODO, check this why not pass
    @slow
    def test_unet_1d_maestro(self):
        model_id = "harmonai/maestro-150k"
        model = UNet1DModel.from_pretrained(model_id, subfolder="unet")
        sample_size = 65536
        noise = paddle.sin(
            x=paddle.arange(start=sample_size, dtype=paddle.float32)[None, None, :].tile(repeat_times=[1, 2, 1])
        )
        timestep = paddle.to_tensor([1.0])  # must cast float32
        with paddle.no_grad():
            output = model(noise, timestep).sample
        output_sum = output.abs().sum()
        output_max = output.abs().max()
        assert (output_sum - 224.0896).abs() < 0.04
        assert (output_max - 0.0607).abs() < 0.0004


class UNetRLModelTests(ModelTesterMixin, unittest.TestCase):
    model_class = UNet1DModel

    @property
    def dummy_input(self):
        batch_size = 4
        num_features = 14
        seq_len = 16
        noise = floats_tensor((batch_size, num_features, seq_len))
        time_step = paddle.to_tensor([10] * batch_size)
        return {"sample": noise, "timestep": time_step}

    @property
    def input_shape(self):
        return 4, 14, 16

    @property
    def output_shape(self):
        return 4, 14, 1

    def test_determinism(self):
        super().test_determinism()

    def test_outputs_equivalence(self):
        super().test_outputs_equivalence()

    def test_from_save_pretrained(self):
        super().test_from_save_pretrained()

    def test_model_from_pretrained(self):
        super().test_model_from_pretrained()

    def test_output(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
        model = self.model_class(**init_dict)
        model.eval()
        with paddle.no_grad():
            output = model(**inputs_dict)
            if isinstance(output, dict):
                output = output.sample
        self.assertIsNotNone(output)
        expected_shape = [inputs_dict["sample"].shape[0], 1]
        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")

    def test_ema_training(self):
        pass

    def test_training(self):
        pass

    def prepare_init_args_and_inputs_for_common(self):
        init_dict = {
            "in_channels": 14,
            "out_channels": 14,
            "down_block_types": [
                "DownResnetBlock1D",
                "DownResnetBlock1D",
                "DownResnetBlock1D",
                "DownResnetBlock1D",
            ],
            "up_block_types": [],
            "out_block_type": "ValueFunction",
            "mid_block_type": "ValueFunctionMidBlock1D",
            "block_out_channels": [32, 64, 128, 256],
            "layers_per_block": 1,
            "downsample_each_block": True,
            "use_timestep_embedding": True,
            "freq_shift": 1.0,
            "flip_sin_to_cos": False,
            "time_embedding_type": "positional",
            "act_fn": "mish",
        }
        inputs_dict = self.dummy_input
        return init_dict, inputs_dict

    def test_from_pretrained_hub(self):
        value_function, vf_loading_info = UNet1DModel.from_pretrained(
            "bglick13/hopper-medium-v2-value-function-hor32",
            output_loading_info=True,
            subfolder="value_function",
        )
        self.assertIsNotNone(value_function)
        self.assertEqual(len(vf_loading_info["missing_keys"]), 0)
        image = value_function(**self.dummy_input)
        assert image is not None, "Make sure output is not None"

    def test_output_pretrained(self):
        value_function, vf_loading_info = UNet1DModel.from_pretrained(
            "bglick13/hopper-medium-v2-value-function-hor32",
            output_loading_info=True,
            subfolder="value_function",
        )
        paddle.seed(0)
        num_features = value_function.config.in_channels
        seq_len = 14
        noise = paddle.randn(shape=(1, seq_len, num_features)).transpose(perm=[0, 2, 1])
        time_step = paddle.full(shape=(num_features,), fill_value=0)
        with paddle.no_grad():
            output = value_function(noise, time_step).sample
        expected_output_slice = paddle.to_tensor([291.51135254] * seq_len)
        self.assertTrue(paddle.allclose(output.squeeze(-1), expected_output_slice, rtol=0.001))

    def test_forward_with_norm_groups(self):
        pass
